def slpFuncY(mslpMean, mslpStd, mslppmm, datefhour, dateArr, lats, lons, date, ind): subsetPerc = np.ones_like(mslpMean) totalPerc = np.ones_like(mslpMean) ssaAnom = np.ones_like(mslpMean) saAnom = np.ones_like(mslpMean) if datetime.now().month >= 3 and datetime.now().month <= 5: mArr, sArr = mc.mcliLoad(var='mslp', ind=ind, notDJF='MAM') elif datetime.now().month >= 6 and datetime.now().month <= 8: mArr, sArr = mc.mcliLoad(var='mslp', ind=ind, notDJF='JJA') else: mArr, sArr = mc.mcliLoad(var='mslp', ind=ind) for i in range(0, len(mslpMean)): subsetPerc[i], totalPerc[i], ssaAnom[i] = mc.subsetMCli( mslpMean[i], mslpStd[i], mArr[:, i], sArr[:, i]) a = netCDFSSA(ssaAnom[datefhour % 24 == 0], (date.toordinal() + date.hour / 24.)) a.detNewApp() print 'completed SSA' print 'starting slp plots' if datetime.now().month >= 3 and datetime.now().month <= 5: [ pt.slpplotMaker(date, mslpMean[i], mslpStd[i], datefhour[i], dateArr[i], ssaAnom[i], subsetPerc[i], totalPerc[i], mslppmm[i], lats, lons) for i in range(0, len(mslpMean)) ] elif datetime.now().month >= 6 and datetime.now().month <= 8: [ pt.slpplotMaker(date, mslpMean[i], mslpStd[i], datefhour[i], dateArr[i], ssaAnom[i], subsetPerc[i], totalPerc[i], mslppmm[i], lats, lons) for i in range(0, len(mslpMean)) ] else: [ pt.slpplotMakerY(date, mslpMean[i], mslpStd[i], datefhour[i], dateArr[i], mslppmm[i], lats, lons) for i in range(0, len(mslpMean)) ] gc.collect()
def slpFuncHist(mslpMean, mslpStd, mslppmm, datefhour, dateArr, lats, lons, date, ind): subsetPerc = np.ones_like(mslpMean) totalPerc = np.ones_like(mslpMean) ssaAnom = np.ones_like(mslpMean) mArr,sArr = mc.mcliLoad(var='mslp', ind=ind) for i in range(0, len(mslpMean)): subsetPerc[i], totalPerc[i], ssaAnom[i] = mc.subsetMCli(mslpMean[i], mslpStd[i], mArr[:,i], sArr[:, i]) print 'starting slp plots' [slpplotMaker(date, mslpMean[i], mslpStd[i], datefhour[i], dateArr[i], ssaAnom[i], subsetPerc[i], totalPerc[i], mslppmm[i], lats, lons) for i in range(0, len(mslpMean))] gc.collect()
def slpFunc(mslpMean, mslpStd, mslppmm, datefhour, dateArr, lats, lons, date, ind): subsetPerc = np.ones_like(mslpMean) totalPerc = np.ones_like(mslpMean) ssaAnom = np.ones_like(mslpMean) saAnom = np.ones_like(mslpMean) if datetime.now().month >= 3 or datetime.now().month <= 5: mArr, sArr = mc.mcliLoad(var='mslp', ind=ind, notDJF='MAM') mArr, sArr = mc.mcliLoad(var='mslp', ind=ind) for i in range(0, len(mslpMean)): subsetPerc[i], totalPerc[i], ssaAnom[i], saAnom[i] = mc.subsetMCli( mslpMean[i], mslpStd[i], mArr[:, i], sArr[:, i]) print 'starting slp plots' [ pt.slpplotMaker(date, mslpMean[i], mslpStd[i], datefhour[i], dateArr[i], ssaAnom[i], subsetPerc[i], totalPerc[i], mslppmm[i], lats, lons) for i in range(0, len(dateArr)) ] gc.collect() datafunc(ssaAnom)
def hgtFunc(hgtMean, hgtStd, hgtpmm, datefhour, dateArr, lats, lons, date, ind): subsetPerc = np.ones_like(hgtMean) totalPerc = np.ones_like(hgtMean) ssaAnom = np.ones_like(hgtMean) saAnom = np.ones_like(hgtMean) mArr, sArr = mc.mcliLoad(var='500hgt', ind=ind) for i in range(0, len(hgtMean)): subsetPerc[i], totalPerc[i], ssaAnom[i], saAnom[i] = mc.subsetMCli( hgtMean[i], hgtStd[i], mArr[:, i], sArr[:, i]) print 'starting hgt plots' [ pt.hgtplotMaker(date, hgtMean[i], hgtStd[i], datefhour[i], dateArr[i], ssaAnom[i], subsetPerc[i], totalPerc[i], hgtpmm[i], lats, lons) for i in range(0, len(hgtMean)) ] gc.collect()
def tmpFunc(tmpMean, tmpStd, tmppmm, datefhour, dateArr, lats, lons, date, ind): subsetPerc = np.ones_like(tmpMean) totalPerc = np.ones_like(tmpMean) ssaAnom = np.ones_like(tmpMean) saAnom = np.ones_like(tmpMean) mArr, sArr = mc.mcliLoad(var='850tmp', ind=ind) for i in range(0, len(tmpMean)): subsetPerc[i], totalPerc[i], ssaAnom[i], saAnom[i] = mc.subsetMCli( tmpMean[i], tmpStd[i], mArr[:, i], sArr[:, i]) print 'starting tmp plots' [ pt.tmpplotMaker(date, tmpMean[i], tmpStd[i], datefhour[i], dateArr[i], ssaAnom[i], subsetPerc[i], totalPerc[i], tmppmm[i], lats, lons) for i in range(0, len(tmpMean)) ] gc.collect()